Discover the Best Medical Receipt Format for R&D

Medical receipt format for R&D

three okay great so as you heard um the recording is in progress um you know you don't have to have your videos on um but uh i think we can get started oh wait so uh would would you like to introduce yourself tommy oh i'm sorry dummy i missed you yeah and any other tas after donny as well i just have a little bit of background noise so it's dummy i'm a graduate student at the university of victoria here in canada um i used to work with the minister of health so that was my first interaction with her that was um last year and um yeah so i've been using hours since 2019 actually nice to be here and julianne would you like to introduce yourself yes thank you hi i'm a fourth year phd candidate at northwestern studying health and biomedical informatics and i use r every day to study health outcomes after stroke and i've been using it since 2018. great um if you have questions just as a reminder put them into the chat so we can have lots of there's all the tas and everybody can interact with them ted just posted again in the chat the link to the rstudio cloud um and so we're gonna get started um ah i will start and then we'll hand it over to daniel okay so can i get a thumbs up if you can see the screen great um uh you'll have access to all the materials after the are they're all available to you in our studio cloud but they'll be available also on github afterwards um so just a little bit of background if you uh if it doesn't fit you maybe you can run away spend three hours here um but why should you come to this workshop um we'll talk a little bit about um why we might want to be using r um we'll write some of your first r if you've never written r or also if it's been a long time well you know if you haven't heard the word tidy verse before um you're going to learn a lot of stuff today um and then we're going to show some things and not dive too deep into them but to just kind of give you get you excited and give you a flavor of what's possible in r and the r equals this ecosystem so i think we can skip this for the r workshop because we're all here um okay um i'd like to take this moment just to pause to say um if um you are having issues getting things set up please message in the chat sooner rather than later um and then danielle do you want to take it from here do you want to keep me or have me keep going with the introduction kind of orientation uh you can keep going until after our studio cloud perfect um okay so ah let's blow this up a little bit i i love this graphic um allison horst is you know a very prolific um i don't even know what the category to put her in but like amazingness and she has this um she's a very good illustrator and i think this graphic encapsulates a lot of like what it is on learning are like when you start it can be really frustrating you're like i can't like which window am i supposed to be typing into like i don't i don't get it um then you start to have some progress and then you start to be doing like kind of like more difficult things and you're gonna hit the valley of despair of like oh i don't get it like ted said like oh maybe this just isn't for me i you know i didn't learn how to program when i was 16 so i've never learned a program false false false um and then one thing i think is amazing about the art community is the the how the high quality of um just random people's blog posts showing what they've done the people on twitter sharing work they've done um offering to help each other like i i posted ones like oh i'm thinking about making a package and i have like 20 different people in my dm saying if you need any help you know reach out uh to me um and then you're just gonna get really comfortable with always learning um ted danielle you guys have little other little tips thoughts uh so the the rstats hashtag is amazing on twitter um i've met a ton of people through it and everyone is super helpful so if you have any if you have any questions and you don't think you can't find the answer oftentimes i will ask it on twitter with the rstats and someone will get to me which is pretty amazing um great so um just giving you a little bit of background on our studio cloud for this workshop we're going to be using um our studio cloud why well our studio cloud is like a fantastic way um to teach r and also to um uh to get people who are new to art up and running without a lot of the friction points that can happen with local installation issues on your local computer so there's nothing that you need to download um with our studio cloud um and this just as a reminder you don't need to use our studio to use r but i do and what lots and lots of people do and it's a very friendly way to interact um with r um the good thing about rstudio cloud and um our studio that you would download onto your computer is they look very very similar so even if you're learning things on our studio cloud those are going to directly translate into it once you would have it on your local machine and both r and r studio are um free to download to your computer okay so at this point i want everybody um to law have already logged onto our studio cloud if not um do so now we're gonna paste it again into the to the chat um uh to go there um either please raise your hand uh in the zoom session or um put a message in the chat when the speakers are speaking it's a little bit difficult for us to look in the chat box but tas are going to be on the prowl for the questions um if some if tas or other instructors happen to see like a really important question please like just interrupt the speaker so we can address it and clear it up for everybody yeah just one thing if you're feeling uncomfortable like messaging the whole group feel free to message the tas individually um because they are really good and you know they can help you so excellent okay um so uh you if you clicked on that link to get into the project you're going to see something that looks similar to this if you don't let us know in the chat um but once you see that that intro to r for medical data um just click on it and open it up um it does take a few seconds to load um and while we're waiting for that um i just want to show you the the orientation of the space that you're gonna see um so these are the four panel panes i guess um that you're gonna see on our studio whether it's on your local computer or on our studio cloud and so going clockwise starting with pane one called source um we'll talk quickly about um what uh what each of these do so source is generally where we're going to be doing a lot of our work um that's where you're going to be writing your code um and we're in this special file which we'll talk about a little bit later called our markdown and you can actually run your code in there as well and see the output up there um if you're creating new files you're going to see um that in there two um over there in the top right corner you're gonna see the environment um and that is gonna be when you create something from your programming for example you read in an excel file and name it something you're going to be able to look over there and see it um as the r version of that object um i guess i should have been more conventional in my naming of numbering of things but then we're gonna go to four from two into files and here's where you're gonna see the files that are associated with um your project um and you're gonna see there's kind of like tabs much like a uh a browser situation where you see things like plots help and viewer and we'll go into that uh and some more details but for there we're going to open something up soon in the files um uh area so be looking there and then the final place here is the the console or terminal both and and this is where you put it just directly enter in code and run it um when you're not like kind of we're doing kind of quick things or or running scripts and stuff like this i don't do a lot here um because it's easy to lose your work when you're down here so um don't worry much about it as you're starting out in the r um you as you get more advanced you'll see what the advantages are of things down there okay so now um for a very quick orientation to our markdown i want everybody to go into this files area right here and look for the intro to r for medical data workshop dot rmd and open that up something like this and make sure it's the rmd and not html or anything else like that um it looks like the little icon around it is like a little piece of paper with a red circle on it um and i guess uh if if people are there could i get some thumbs up in the in the in the reactions just to see if people are getting there great great um okay and well this the document that you're seeing that you've opened up from that is not the same as this image here um i want to talk about just some of the very generic components of an r markdown file so you can imagine an r markdown file is a combination of code and tech like text um and there's like finer details so things about that um and so the big three chunks you're going to see at the top of the document is going to be something called yaml and we're not going to really talk anything more about this but just know that there's ways to make your um documents super customizable with multiple authors like how you want citations formatted just like crazy stuff but for right now you don't really need to know anything more about yaml for the rest of the day um and then you're going to see these gray sections of the document that are offset by these um back ticks and some curly brackets here and this is a code chuck and it means that you have put code in there and then you can execute that code by hitting the little run button which is the the triangle over here um and then the the final component is this um text which is it's really just text as if you were typing in a word document and an email space um you can use a markdown syntax r has its own little dialect of our markdown but very compatible um uh are very similar um i think i've said all of this danielle ted if you have other things to um say jump in now or else i think we're going to have daniel start talking about it exploring some data okay um i am going to stop sharing and unless daniel do you want me to share my screen of the rmd are you going uh i i can do it perfect i'm gonna stop sharing um hopefully this works all right okay so uh what mara had said that hopefully you have the intro to r for medical data workshop dot rmd open um if you scroll down a little bit you'll notice that there is like a published version of this a lot of the things she was saying was already covered in the first up until um about 100 uh line 147 so everything that she showed that was rendered all nice and pretty was essentially the document that was rendered up until this point um right now since there are pieces of this code that we are going to work together and fill out uh you won't have that pre you won't have the ability to render this without um having errors happen because we literally blanked out and invalidated invalidated our code but hopefully by the end of this workshop you should be able to render and get like a nice pretty website view of everything that we're working on so at least for me um and if you didn't add in any new lines or anything at about 148 you will see your first um r block that we are going to work with and so you'll notice that these back ticks so on a standard us keyboard it is the key to the left of the number one it is a backtick um i believe if you're on like the az erty uh azerty keyboard it's like where the number six is i think it's somewhere around there um but between these two uh sets of backticks you'll notice that in the rstudio cloud or in our studio the background is going to be a little bit different it's going to be grayed in and this is your visual cue that our studio is understanding this block as our code that you can run versus regular prose text that uh will be displayed um in like a nice pretty format and so in this set of uh these blocks you can run or you know r is a glorified calculator so you can run this code in one of two ways on the right hand side you can click this little play button and you'll notice that it will take the code that's in here which is right now one plus two and it will return three at the bottom and i'll make this a little bit bigger since if you are planning to follow along um we are going to be uh writing a bunch of codes so if you do want to write notes to yourself you can use this hash symbol so that is shift and a number three on your keyboard and you can write a comment and this will be considered code that r won't really execute right so like the text here is a simple calculation isn't actual code it is a comment for you since we are using rstudio cloud what you do want to do at the end of this workshop and we'll mention this at the very end as well you do want to select the uh our markdown document on the right hand panel click on more and then go to export and actually save this document to your local computer because this rstudio cloud system is not going to be there forever so if you do use this document to take notes to yourself um it is uh remember to export it um another option is if you see up by um danielle's um avatar in their right hand corner their little left to that is something called save a permanent copy and if you click there you will have a copy of um the space for yourself ah yeah um that is another way um all right so um throughout here because this will end up being rendered into like a nice prettier document um with uh screenshots of things um there are going to be portions up here that aren't really going to be our code in the sense of code that we're going to have r run but it's going to say something like include graphics which if you do want to run a piece of our code like one plus two what you can do is either have your cursor on it or um press this little play button if you do you can select the code that you want and hit control enter and it will take all of the code that you have selected and run it at the bottom right so there's multiple ways you can um uh run these little code blocks and you can see if i ran this little code block that put in include graphics it's gonna say like here's the actual picture and it renders that image um underneath so that's the basics around um just navigating this space um so what we did is um the format of teaching that we're working with right now is we're trying to give you some instead of typing everything from scratch uh you should be able to have this document with you with most of the stuff filled out and so uh you can follow along uh without typing everything manually and it in some sense it will save us a little bit of time and our main job right now is just to get you exposure on that little first hump of the r uh learning curve all right so as um most people who work with data um who aren't like quote unquote like coming from the data science world we're probably working in excel files right so we are very uh very often are working in excel because that's you know that's the thing we use to look at our data we can do run one-off calculations in excel like that's usually if you've never programmed in a programming language to work with your data that's what you're doing um so in r there is a library called read xl and this is a library that gives us a function to load up excel files so if we sort of select this block of code hit control enter you'll see on the bottom left hand side here it will load the read excel library in our current setup that we have right now there is on if you go to this files panel you'll see that there is a data folder and there is an excel file called smoke underscore complete.xlsx and you can see that little portion here of going into the data folder pointing to the excel file is right here where i have selected in line 172. and so because we loaded up the read excel library we are now given this read underscore excel function and if i just select this little portion here and run it you'll see that it will load up this excel file from our computer so try this out on your own excel files um if you'll notice that if you have an excel file with different sheets there is a way to load up a specific sheet but in r if you have multiple sheets in an excel file you essentially have to load them one at a time there it's not like in excel where you can open up an excel file and then have all of those sheets available to you at a time all at once um that is part of it as uh just good data practice like one file one data set um there aren't for at least for this class there aren't like these big giant batches that um that you that get loaded all together automatically you have to do it very manually so you'll see here that if we just run the read excel file at the bottom it has printed out a nice pretty version of this excel file this is useful we can load up an excel file but as we're working with data we don't have we don't want to re-load this excel file every time we want to do a little calculation right so if we look at the rest of this line we're going to create a variable called smoke underscore complete and then you'll see here this little arrow symbol and it's a good practice to put a space between both sides because it is literally the less than dash symbol and you don't want to confuse yourself with this dash as being a negative number in this case it's pretty obvious that there's no such thing as a negative read excel call but part of this is good coding practices to put spaces around that arrow so we're going to say smoke underscore complete gets this entire read excel line so if we click this little play button one thing you'll notice that it no longer prints out the excel file that got loaded but if we look at the top right hand panel there is a variable that got created for us called smoke underscore complete and we got a few simple statistics about what's in here it's telling us that we have 1152 observations also known as rows and 20 variables so that is 20 columns so we've looked at or where we have loaded up this data set the next thing that we want to do is to just to make sure that this thing got loaded up correctly right so there is some text underneath about um how we can do this but there's a couple ways um that we can first to explore whether or not our data set was loaded up correctly there is another function you might have heard the term tidy verse um tidy verse refers to a set of packages primarily maintained by our studio the company and it's a set of packages that all relate with working with data and they all integrate very very nicely with one another there is a within the tidy verse there is a package called d plier or that is how you pronounce it d plier so just like how we loaded up the read excel file library to load up our excel files we can load up the dplyr library to load up just the set of packages related to processing data gonna see a bunch of red text here not every portion of red text that r gives you is an error um that is something that a lot of newcomers get very confused about um they say like oh i got this is it an error that's sort of one of the things i wish are like sort of changed that errors and messages actually don't show up as red because it's not actually a problem but you'll see down here it is attaching the uh d plier library and you're gonna see some things about uh functions being that the dplyr library over uh overwrote right but for this class and for the most part when you load up the plier or if you load up tidy verse uh this is pretty common all right so we loaded up our excel file one of the easiest ways that we can look at how this data set was loaded was if you look at this top right environment panel and you can switch to other panels but for the most part we're going to be just clicking on this environment panel if you click on the word smoke underscore complete or for me if this is the first time you're using rstudio click on the little spreadsheet icon do not click this little green arrow that will do something differently but if you click on any other part of that line it's going to open up the excel or spreadsheet view of the data set that you loaded right so you can see everything that's in this data set you can scroll all the way down to the last row to see all of your columns et cetera et cetera right so just because you're using programming language rstudio does a very good job in sort of you know getting you a view that you're sort of used to and this is a really good thing when you first load up a data set regardless of what data set you use and i do this all the time as well is especially if you are working with a data set that you know you've never seen before most of the problems are going to happen like within the first couple of lines of your data set so one thing you want to make sure of is did your columns get loaded in correctly it's very common that sometimes people take notes in the first couple of rows in an excel file or in any data set and then now your column names are going to be like those rows those notes and then the first row of data is going to be like your actual column names um and then etc etc so everything will get all messed up um so one of the questions i just wanted to quickly address um a question that came up in the chat um somebody asked oh we don't have to the package we can just load the library and that is true because i when i set up the rstudio workspace i installed all the libraries packages that we would need so you don't have to do that now um when you're doing this in your own space on your own device you will have to packages but just to keep the friction low i installed all those things um ahead of time so hopefully we won't have to deal with any of that yep and then if you ever um so the actual message in chat like you actually knew the command to packages but if you ever forget our studio in the bottom right hand corner there's a packages tab and you can click on and pipe away um so that's also an option if you don't remember the actual command all right so at least in our data set we can now see that okay the first and the most important thing our column names look correct they got loaded incorrectly and the first row actually looks like data um and these are things that might happen depending on like if you're working with people who aren't using a programming language to analyze data there's going to be like weird quirks um especially if you're in the excel world like you're gonna end up with like a random uh a bunch of empty columns here and then like a number because someone decided to put an off calculation somewhere or at the very bottom of the data set you know there might be like the sum or some average calculation of the entire column and so those are things that you want to be very mindful of uh when you load in especially someone else's excel file right so make sure that those off calculations that probably happened aren't there and then this is really good for you as somebody who is now trying to use r in part part of their workflow to don't make those one-off calculations if anything put them in another sheet so you don't load it up as part of your actual data set i pasted a link to this wonderful [Music] basically this article by kara wu and carl broman about spreadsheets and good practices for formatting them it's surprisingly really entertaining so it's a good guide yeah it's it's entertaining because we've all done this and um they sort of call you out on it so and another thing daniel said about you know oftentimes at the beginning of your excel spreadsheet that you'll see things that are you know like comments or stuff like this but then also it's very common in a lot of medical data especially if you're getting outputs from like um enterprise data warehouse that comes in like an excel spreadsheet that like the very end of your document will have like a timestamp and like this document is confidential or something like this and so you when you try and load it you'll start to get errors at the end so i really recommend what you ted shares is will save you a lot of pain all right um so another way instead of just actually clicking on this um if you don't if you're not using rstudio for for some example for whatever reason or if you just want to quickly look at the first couple values for all of your columns in like a very quick view d plier gives you this function called glimpse and so if we run this block you'll see that it sort of gives us like a transposed version of the actual spreadsheet so like flip the rows and columns and so this is a really good way to get a quick um just so you can scroll down and not scroll across for things it gives you the number of rows it gives you the number of columns and it gives you all of the column names because we're in the tidy verse world um it's also giving us what is the type of data stored in that column so if you for example get something um excel does this a lot where if you put in like a like a number for example all of a sudden turns into a date you know like this will actually tell you what did r load this data as right so it's also really common uh for zip codes to be loaded in as an actual number and some zip codes start with the zero and then now you're missing digits in your in your zip code right so this is a good way to sort of start like figuring out what might be wrong with your data set and so we don't i don't think we have that problem here but these are the types of problems that you might show up so chr stands for character so like actual strings so you can see here your smoke is showing up as a character right and so we something you know later on when we're looking at this data set is like oh why did this thing get loaded up as a character it could be loaded up as a character because there's like n a the word like for missing value was showing up in our data set or maybe somebody typed in missing my ssing and then now that whole column is read in as like characters right so you can get a lot of information about which columns might have problems just by looking at how this data set got loaded in and then not only that you also got a view of a couple of values of your data set as you're um going through this and you can see it's much easier because i can just scroll down instead of going to another tab to like see my data set so let's take um a couple of uh let's take a minute um there's three questions down here and if you want you can type them into chat like just say one and then the answer but using this output you can start answering these questions like how many rows of data do we have in our data um somebody have an answer uh there's multiple ways you can um go about looking at this right so like yes there's uh 1 152 rows in our data there's a couple ways you can do this again you can look at the environment panel here you can look at the output of glimpse one of the other comments was there's this n row parameter so just like read underscore csv as a function there's another function called n row for number of rows and then yes there's an n call for number of columns and if we put in uh smoke complete it'll give us the actual value back right so all of what's really nice about using a programming language is all of these things that you can visually inspect you can write code for and this is really great um especially if you're working or trying to get data that has a very specific structure like if you're working with county data there's only some there's a fixed number of counties like today uh in any different location so your data set should have x number of rows or x number of columns one for each county so the other question is how many columns are there so what are the some of the column names so there's some solutions out there so you can see the number of columns you can get that information from glimpse you can look here um in the top right corner in the environment panel and then some of the values glimpse also gives this to you and then if you want you can click on the actual thing and you'll get the view of your data set so you can get a sense of what's going on and then the other column is like can you tell what type of variable is stored in a column so there's a couple of ways using glimpse you'll see it right next to the column if you are using a function that is coming out of the tidy verse just typing in the column uh the data set itself will give you a small print out that will fit the co um that will fit the cop console and it will give you the number of rows number of columns the column name and then underneath the actual column type um this doesn't always happen and this only happens because we're working with a tidy verse version of a data frame and so this data set that got loaded is called a data frame object and so that's sort of the nice thing with the tidy verse is it sort of allows you to do these quick glances of things like a little bit faster instead of typing the code for it all right so the next thing is we just loaded up a data set um how do we just get a very very quick uh glimpse or a very quick view of like what's going on in here we've already checked that our data set was loaded in correctly and so there is another function called skim that comes from a library called skim r so a lot of our libraries just have the letter r in it as a r is a very punny language and so we can just like loading up all of the other functions there is a library called skim r and we can skim our data set like to quickly read through our data set so the skim r package has a function called skim and this is really good if like you don't do anything else with your excel data analysis you know try loading this into r and then running the skim function just to make sure that uh things are loaded up correctly your data set is formatted correctly but it also gives you a lot of statistics for all of your columns which is something a little bit more complicated to do in excel and so if this is all you do i would consider that a a win as well one of the questions was does it cause problems loading into many libraries um the short answer is no uh there isn't a problem when you load up too many libraries eventually when you start developing packages on your own the name of the functions that you write in your own package will matter because if you remember from the very beginning when we loaded up d plier um i guess like that text isn't there anymore um it said something like this like the following objects are masked from package stats packet stats is uh there's a stats library that is default in r and so it has its own filter and lag function and because we loaded up d plier it overwrote the stats filter and lag function so in some sense if you work in the tidyverse world because it's all maintained by our studio and they are very conscious of the stuff that they're building you don't have to worry about loading too many libraries from the tideverse world if you start loading packages from a bunch of random like quote unquote random people you might start seeing messages about functions overwriting one another in that sense you have to be more mindful but it's not going to cause a problem um okay so skimming our data set um just like using glimpse we get some bit basic information here is the data set name it's called smoke underscore complete it's got the number of rows number of columns it also tells us you know how many of our columns are characters like just regular strings how many of them are numbers this is really good because if you do expect all of your data to be a number like if you're reading in sensor data and it's all just a bunch of numbers um if you end up with something that's a character that might be kind of like what is going on here a lot of medical data sets will have a data set that is all encoded and in a separate code book so you might have the data set itself is supposed to be all numbers and so this is one way you can check like is everything a number without you manually scrolling through everything there's a section here called group variables we'll talk about that uh when mara talks about dplyr stuff but down here the really nice thing and it's a little bit more difficult to get this type of output in excel is for every single column in our data set um in different portions so characters are things that are like our strings it'll give us a couple of descriptive statistics like how many of these character things are considered missing we know that just by looking at the glimpse or that quick view of our data set right here some of the character um has n a the string like the characters and uh followed by a so this is usually r sees this as a missing variable but it's because we see it in quotes it is not being treated as a missing variable so in some sense we can see like this small discrepancy between okay this is this is read in as um a value called n a not missing value um that are understand so we'll see that like okay the number of missing all of these being zero kind of suspect because we know that there's a problem there the completion rate so this is out of a a percentage really so it's saying that we have nothing missing and everything has a value and then in here we have um some other sets of um uh descriptives i believe this is min max in terms of like number of characters so if you see something that's like extremely long um probably someone put in a paragraph or like a for something that's like an other response or something yes and it's extremely suspect to have no missing data um in medical data um just just you know like if you look at like gender and pregnancy values like you expect the males not to be pregnant or like an n a value there um because that doesn't apply right so um a lot of times especially if you're getting this stuff out in the wild it's very unlikely you have nothing everything as completed data so the next block and let me make this screen a little bit wider so this prints out a little nicer the next block down here is a different set of summary statistics and it's different because we have different types of data right because r understood this as a number when we have number of values in our data set we typically look for different things so we have here age act diagnosis days to birth cigarettes per day those are usually numbers we get the same basic things of like is there a value missing and its completion rate but because it's a number uh we usually want to look at the mean and standard deviation of these things and so using glimpse is a very great way to see stuff that you probably already want to figure out when you load up a data set right and so ajax diagnosis you'll see that this is uh 24 000 so probably something to do with a unit there that's you know maybe clearly this isn't year as um the unit um it's the actual its age at diagnosis in terms of day and so if we want to figure out how old they are in number of years we have to do some kind of calculation it's really dividing by 365.25 which tomorrow will show you in a little bit and we get like um percentile values so we have quartile so what is the lowest value the 25 quartile a percentile the 50 tile 75 100 and at the very right hand side we actually get a um ascii histogram just to show you what is the general distribution of this data set right and so you can see for ajax diagnosis we know it's skewed to the left it's i know this data set a little bit so it's like in the 60s right so this data set are mostly like the people between 60 and 80 um days um so for example cigarettes per day um just looking at the mean and standard of efficient deviation uh it's around two cigarettes per day um and so most of this data is going to be squashed um to the left-hand side so that's right skewed and that is what skim is allowing um allowing you to do is very quickly get a view of your data set if we um if we go back up to our um read underscore excel data set in our studio if you put the cursor um between the uh right before that opening bracket and you hit the f1 key the rstudio will open up the help pages for your function that you have the cursor on and so this is here we're using the read excel library and we're using the read under underscore excel function you can see down here this read underscore excel function takes in actually a lot of other ways you can tweak this function the first thing is path which is that data set that we loaded but you can see here you can say sheet equals and if you want to load up a different sheet you can there is another thing here so n a is a empty string but if we were to say n a is equal to the string n a we'll now we now would have fixed that little problem of all of these things that were characters are now actually being properly read in as n a right and that's because the excel file itself had n a the characters being read in and usually people assume if it's blank it's missing and so um you'll see this a lot in health data sets as well where 99 gets encoded as a missing value um and so that's like missing i as the person putting together this data set understand that this is missing and it's different from i as the person forget to collect that data right so you'll see 99 or 88 um a lot for like other random codes um you'll other you'll also see um i believe range is like how you can set like where your data set actually starts um so if you have a whole bunch of metadata in the beginning of your excel sheet or at the end like if you get data from like the cdc like the first sheet that gets loaded is just like here's the terms of use right so you're not loading from the first sheet you're usually loading from a different sheet and so that's how you can um if you for example put in a data set into read excel and all of a sudden there's something wrong with it you can put your cursor right before the opening parenthesis hit f1 and then use the parameters here to help you and if you scroll down a little bit more you'll see the actual read the actual definition or the of what each of those parameters does right so you can specify and read that as well so here n a is a character vector of strings to interpret as missing values right so if i was loading up uh miss um a health data set and i know that in the code book it says 99s are considered missing i would also put in 99 and so when that gets loaded into r it also gets treated in as a missing value right and so these are different ways you can modify how the data set gets loaded in and if you can fix it during load in time it'll save you a bunch of headache so because you don't have to process it manually you're using uh this function for you to load in and so yeah i will post this little thing in oops into chat whoops whoops um for that bit and so i believe uh so do we have any questions so hopefully this got everyone um oriented into just loading up your data set into r and if all you do is take a data set that you have right now and just try loading it in try it look at it through glimpse and skim if you've never programmed before i would consider that a win because now you have a very a little bit more programmatic way to um spot check your data set um and you get some kind of descriptive stats out of it and then you can slowly start picking up more our skills but getting started is you is usually the hardest part of all of this so there's a question from uh jason toppin so i know you can read use read csv for excel files is that a good idea um so the answer to that is usually no uh so i believe okay so if you do use a function that's like read underscore csv it's gonna treat it's gonna assume that one it's a plain text file and it's delimited by a comma csv stands for comma separated values right so excel files one are technically aren't really plain text it's you can open it up as a plain text link but it's really not and so if you try to read an excel file using read underscore csv it's probably going to error out i'm going to say that like the other side is usually okay like excel will open up a csv file and you can use like the text to columns feature or most of the time excel understands that it's a csv file and it'll open up but usually the the other way it doesn't work um if you do end up opening up what you think is an excel file with read underscore csv i'm almost certain that they just changed the extension on you and so you didn't really get an excel file to begin with just one quick thing to mention is if you do want to use read csv you can convert the excel file you can see within excel you can save it as a csv file and then use read csv to read that new version of the file yeah and one other thing um before we take we sort of switch the next section if you ever forget what how to load up a data set for example in the environment panel there is this portion here that says import data set it's different from the import data set like up here somewhere that i know exists but i never used but this portion there's part that says import data set so if you have a spss sas data data set excel if you forget the function or you don't know the function to use you can use this system as well so we can say from excel it'll give us a nice little pop-up and then you can actually browse to your excel data set right so we have data excel and it will give us a preview of what it will load and so this is useful because if you have multiple sheets you don't have to rely on your you typing it you'll notice that right here it's going to write the code for you on the side and so if you have a range like okay excel sheet from like let's say i want a1 to i don't know d d5 um you can do little subsets of your data set right here and you'll see that it's going to write the code for you and so what you can do is copy this block of code you can also hit import as well and it will do that for you and then you can simply paste this into r and you have that code block for you so that that works for a bunch of other things as well um so don't feel like you have to memorize every single function um that we're showing you um there's a lot of ways to get help especially from just getting your data set into r and that little import function is really useful um and hitting f1 is also really useful um all right okay um we are getting close to the top of the hour um so i think this would be a good time for a file break uh i'm gonna uh ted daniel do you have anything quick to say before people go and then we can restart in five minutes uh no we'll take about five minutes um if you have like weird setup issues just plop them into the zoom chat um and we can either help you set things up right now but if there's a more general question we'll answer when we come back okay so a question came up in the chat during the break about um how do you get data for example from github or other sources into rr studio and there are tons of different ways um to do it just you can do old-fashioned ways where you're downloading into your files and then reading it in but then there's also a whole suite of different functions that can pull data in if you feed it like a ur url there's a package i like to use called google sheets for that pulls stuff in from a google sheet that i have um so there's lots of different ways you don't have to have everything locally stored on your device yeah um okay okay ted yes so um before we kind of get into the plotting i just want to again you know it's all about kind of words of wisdom uh can everyone see my screen by the way sorry i need i'm just trying to get to this yeah i can see it ted okay great um so i'll send the link out to these these are this is just um some slides about errors and debugging um that again you know we want to encourage you to like you know keep on trying so number one thing is you know learning r is not easy so kudos to all of you for you know wanting to do this so again like the the key is really what to learning more and to finding out like the source of errors is to not beat yourself up um the the number two rule is like use google and don't feel bad about it because um we all use google because sometimes there is just like a very obscure error and like you know i will often cut and paste that error into google and see if i can find an appropriate help but this is just a cute another cute um uh drawing from allison horst that just you know talks about kind of the the debugging process so sometimes you really you really got that you think you got this um but it is you know there's all of these other kind of again it's kind of like that roller coaster of kind of going up and down um just want to talk daniel covered this a little bit but i just want to talk a little bit about understanding the difference between warnings and errors so oftentimes like what daniel was showing you you you get a warning and a warning is just an indication that the data or arguments aren't quite what the function expected so oftentimes you can run the code but you should definitely verify the output of the code so the difference between a warning and an error is that the narrower means that a code can't execute at all given what you you you've kind of put into the function so this kind of gets into why these can be very difficult to understand so googling is standard practice for errors so again um you know if we're if we i know most of your clinicians so we can talk about levels of evidence right so there's kind of an or there's an order of levels of evidence in terms of googling as well um a lot of the times this is kind of the order that i look in so i go in in terms of our studio community if i have a tidy verse question they are great um i could usually search like the search the forums there and i can usually find an answer stack overflow is also good that's kind of my second second line of googling so this is a website that has a lot it's basically a knowledge base when people encounter errors they ask questions about it and hopefully someone has answered it um so that's also a great resource um so if you are doing bioinformatics another great source is biostars and then kind of the the last thing i check is the packages github page so um you know the rst the tidy verse has great documentation and you know if something changed in one version to another like that's where i can find out about it um just you know just a plug for social coding i think one of the hardest things like when you're starting out is like being vulnerable and working with other people but i will say that it will improve your code a lot so we all have blind spots and you know if you're working with someone else you know they don't have that kind of that kind of gog those kind of you know code goggles that you might have so they can usually find something that's like a misplaced parentheses or bracket or misspellings um and then last thing is usually the error you're looking for is at the bottom so there's usually be a bunch of errors generated but the one you're interested in is usually the one at the bottom the last one um and i think that's all all i have so i will stop sharing and take it away mara okay um thanks so much uh ted um so i am going to share my screen okay um can i get a verbal cue that i that you guys can see my screen yep um uh so we're gonna start with the looking at um doing some plotting um and i just wanna draw your attention to a handy little feature um that i didn't know about for a long time when i was using our studio if you see down here where my cursor is there's this little box down here and if you click on it um it has r studio is interpreting different lines of your code to like provide you a table of contents um so that you can um pop around more easily um because sometimes with these very large documents um you are you are uh really seeing quite a you this is just so too difficult to scroll through everything so um i'd like everybody to uh come down to part two the plotting our data um so we're gonna take that data that we loaded with um daniel and we're gonna make some plots um so the first thing we're gonna do is just produce a histogram from that smoke complete using um something called geom histogram and what i want to emphasize is we're just going to make some plots right now and then we're going to talk about the underlying structure of like where these plots are coming from um so looking down in this code chunk right here um i want you to find um and we're gonna define all these terms but i want you to go and put the cigarettes per day variable that's the column name we have in our in our data set and i want you to put that into this underscore um area completely replacing this this is not valid code it's just a place marker that where we want you to put in that cigarettes per day and then once you've done that i want you to hit that run the current chunk chunk and see what you get and then um i cannot see the thumbs up right now but if people could give thumbs up if they're having success with this and somebody give me some verbal feedback um i guess i might be able to see it in the chat okay yes great i'm seeing some thumbs up um and i'm seeing a no okay so uh give it just a few more seconds and then i'm gonna show you um my uh my answer to it okay so i'm going to go in here and i'm going to assign um cigarettes per day to that x and then i'm going to run this chunk right here okay one of the questions that came up was why didn't the plot go to plots um could you just make sure you describe that i mean it's because it's in the rmd file and it's previewing it for you in there instead of in the plots correct correct um a lot of people um and it's hard for me to say this necessarily or describe this because i didn't know what like a code script was for like the first six months i was learning um r like the only way i ever interacted with r was in rmd files and so i was very used to this kind of like i type in my code in a code chunk and then i see the outcome when you're interacting more with a script or in the console you're gonna um see a lot more things like being sent to like the plots um package or i'm sorry tab and instead of being plotted out over here okay um okay going on to the next step unless if of course keep putting your questions great um uh in the chat um so we're looking at this plot right now but there's a few issues with it um i'm actually gonna try and make this i made it big to um make it easy to see but it's almost too big just a few issues um the the cigarettes the titles aren't like i mean they're fine but they're not super pretty um and uh there's no title um so let's try and make our graph a little bit more um descriptive and so we're going to work on just putting some titles into different things and then also we're gonna introduce something called um a themes um function uh and see the outcome so uh i want you all to go down to the the choke code chunk called um beautify plot2 and i want you to put in um any title you want uh for the the title um in this this labs function here and then you can decide what you want to call your x-axis title and your y-axis title here and then i want you to take um a theme classic and plop it into this bottom underscore there so i'll give folks a minute here otherwise i'll just um uh i'm pretty impatient um and so it feels like when you're teaching sometimes uh you think you've waited five minutes but it's been about five seconds and definitely in the um give a thumbs up if people are getting it or i know if you're not in the reactions and once you've put in all those things go ahead and run that chunk again okay a few about 20 more seconds and then i'll um put it in with what i had okay okay so i'm just gonna say this is a great title fantastic x-axis title and i'm just gonna run it then and so you're seeing here where i put my title i'm getting a title up here i'm getting my y-axis title over here and a really nice fantastic x-axis title and just looking back we haven't changed anything about how we're displaying the histogram data itself it's just like other components of the plot so you can hear see here with this theme classic it's a lot more white space and so there's all sorts of built-in custom themes and you can also even create your own themes that your organization um you know like the economist uses are i believe and they have their own um uh package and there's different universities that have it taller their fonts and colors and everything like that so um going on um you're going to see a message not an error a message um of the code that says stat been using bins equals 30. um and so you'll see that here if you click this red text again which is not an error it's just a message danielle and ted have both talked about that um and ted or daniel correct me if i'm wrong but i'm pretty sure like i can't remember where this comes from if this is just a default or if it's something about the data that the ggplot um it is the date it's the default which i'm not a big fan of to be honest but um that's what it that's what it does so yep um uh so you know this for meaning that um so we want to play around with that a little bit to to get a different number of bins meaning sometimes it's easy to get confused because sometimes people refer to bid width which is like how many values are captured in each bar of the histogram and then the bins which is like the number of columns that you could potentially have here so let us um decide that we want uh our bins to be something else in width um so in here under this and this underscore um go ahead and put in two there and and then run your code chunk and see what it looks like um and while everybody's doing that um i'll just make a note about um the see if you'll see this uh red little circle with that x through it and this is the r studio um uh interface telling you that it feels like something is incorrect right there and so it's kind of guiding you to where you might need to make changes so are people having success with that i'm going to put in two there and run the code and then how easy it is to be able to interact with your data and very quickly um change your plot um without uh without all the difficulties that creating plots and something like excel it might feel like um oh i have a lot of control over things but it ends up becoming a very manual process and so one of the great things about creating plots um uh in r and with ggplot is that you can really make complicated things very quickly reusing code or you can make big changes with this tiny tweaks of the code yeah uh just as a quick note like so um we're we are publishing a paper and i did i generated all of the plots using ggplot code and we basically had our students do all of the customization so our collaborators wanted all of these things fixed so i was able to give my students the ggplot code and they were just able to easily modify it and format it as like our collaborators wanted for our figures um which is not trivial when you are doing it for 24 figures not at all not at all um and so let's one more thing we're going to do here um before we get into under pulling the um curtain back a little bit on uh ggplot2 to understand what's going on for here uh i want you to put the variable gender down in this um facet grid area and then run the code we'll spend about 30 seconds on that oh great that's a great question um what does the first line of the code chunk mean so the curly brackets um and then r means tells the um uh computer that we're running a code chunk in r um we're not going to talk about it here but there are other options um in rstudio that you might be running different codes i'm sorry different programming languages so you can run you can use python or sql bash in here and so that's just a a way yeah yeah it's just the the denotation that this we're using r then when it says facet right now that is mean giving a name to the code chunk um and so uh there's some minor not super important things right now but um you for when you name it um that's what creates this code chunk um name down here so you can easily jump around otherwise it just gets a generic um chunk 17 and wouldn't have a name right here um i recently learned that i shouldn't be using underscores in them um i should use dashes if you're going to be like cross-referencing uh different things so if you're writing a paper all in our um and you want to be like refer to a figure or a plot there's this wonderful other packages where you can just say like um refer to facet and it will like auto label whatever figure that is and things like that so really fancy options okay and so we're going to put gender down in here and run that um and so you're going to see here that we have now fasted our plot pulling out the data for females and males in our data with two plots next to each other all the females all the males and this is possible for any number or you know any variable that would be a reasonable thing to do to understand the differences in different groups um and you you've probably seen this a ton in different um uh journal articles or things like this um really uh once you've seen gg plot you'll start seeing it everywhere in article scientific articles okay um it is almost 4 30 right now and we're going to go on to um gg plot 2 i'm talking just about the fundamentals of this package um i know ted has to head out in a little bit so i was just wondering if anybody wants to uh any of the instructors or tas want to mention anything before we started on the next section no um unfortunately we had a scheduling snafu so i do have to drop off i will be back in an hour so um anyways it was good to see all of you and i will see you later bye ted thanks i have something quick to to add um one another way besides at the bottom of your um screen to jump to code is you can use the little lines up near the blue eye um with mara would you be willing to show where that is yeah you can open the little lines right next to the left oh the table of contents i heard yeah and i was like it looks like an eye yeah so if you click on that that's another way to jump to anywhere in your code yeah and then um a really nice thank you so much was that molly i it's hard for me to see um i apologize it's not molly who said that but another nice thing in that when you're in the rstudio iede is that um an ide just stands for integrated development environment i can't remember again yes that's right yeah and that's just a fancy word of saying it's like the graphical interface of interacting with everything um it often if you hover over things it will tell you like what it does and also the code shortcut for things um and i must admit i'm like not i was not a big like keyboard shortcut person until i started coding a lot and like my life is so much better um and i uh if you uh do a google search for just like uh gg plot cheat sheets or our studio cheat sheets um our studio puts out a a big collection of cheat sheets for very popular big packages um that i use all the time and i actually think a whole bunch of them just got a huge um facelift um this summer and and they're super useful um when you're after you've gotten a little bit familiar with the package um to jog your memory about stuff okay so going on uh now that we've made a couple of the plots i want to talk a little bit about um uh what ggplot stands for and so um ggplot is a library it's actually called ggplot2 um is the is the package name and the gg stands for a grammar of graphics and why this is so important is it's really breaking down graphics into very specific um components constituents to make it easy to create graphics to create graphics that can be um understood across packages um and this is just an amazing uh improvement on things before where everybody kind of had their like own package that did some graphic stuff but wasn't compatible with other graphics so well gg plot 2 is a great package there's tons of other packages um that build on to jiji plot 2 and compatible with gtg flat 2 to make all sorts of different things using this consistent way of describing things um and so looking down at this graphic um here sorry it's a little hard for me to read up here and look down at the graphic we're going to talk about some of these different specific parts of graphics and learn a little bit more about how to make them um and then just as a note there's a lot of different ways that you could write um our code or gigi um plot code um there's some sort of like for very formal ways and some more casual ways um and we're gonna focus a little bit more on the formal style just because it's it's we expect a lot of you this is the first time where you haven't seen it very often um so it's a little bit more wordy than you have to be but just to try and be very clear about um what what is going on but as you um become more familiar you're going to start shortening it up and yeah danielle just made a good point in the chat um ggplot 2 is the is the name of the package in the library that we have loaded and ggplot um that you've seen here um is the function um that uh starts making the graphical uh object okay um so uh we're gonna break this big big chunk of code down to unde